class_docstr: This class implements a residual block.
  
  Arguments
  --------
  dim : int
  Input channels of the tensor to process. Matches output channels of the residual block.
  
  Returns
  --------
  Residual block output : torch.Tensor
  
  Example
  --------
  >>> res = ResBlockAudio(128)
  >>> x = torch.randn(2, 128, 16, 16)
  >>> print(x.shape)
  torch.Size([2, 128, 16, 16])
docstr: Implements a residual block.
docstr_tok: ["Implements","a","residual","block","."]
code: def __init__(self, dim):
      """Implements a residual block."""
      super().__init__()
      self.block = nn.Sequential(
          nn.Conv2d(dim, dim, 3, 1, 1),
          nn.BatchNorm2d(dim),
          nn.ReLU(True),
          nn.Conv2d(dim, dim, 1),
          nn.BatchNorm2d(dim),
      )
  
imports: import torch
  import torch.nn
  from torch.autograd import Function
label_desc: Use image node_id 1 to create a new ResBlockAudio object from inherited base classes: nn with example: obj = ResBlockAudio(dim)